Machine vision systems are increasingly employed to replace human vision in a wide range of industrial processes such as manufacturing operations. A machine vision system typically provides automated, computer-based image acquisition and analysis capabilities that can be employed for tasks such as measurement and inspection of parts or materials. For such tasks, a machine vision system typically is configured with a camera for acquiring an image of an object of interest, e.g., a part being produced, and further is configured with processing functionality to process the acquired image and produce information about the object. Frequently, in measurement and inspection tasks, the object image acquisition is tailored to include specific object features that are of interest for a given analysis task.
There exist well-established machine vision techniques for acquiring and analyzing an image of a substantially two-dimensional object or a substantially two-dimensional face of a three-dimensional object. Here a camera is optimally positioned orthogonal to the plane of the object or object face under consideration to acquire an image that includes the entire object or object face. Features across the entire object or object face are then in full view in the image and are together available in the image for analysis. Even perspective image views acquired at a non-orthogonal, i.e., oblique, camera position can generally capture a complete view of a two-dimensional face or an object that is effectively two-dimensional.
For many three-dimensional parts and many manufacturing environments, this full-view image acquisition cannot be accomplished, however. Specifically, for complicated three-dimensional object shapes, and more specifically for complicated opaque objects, and for various feed material and tooling configurations, one or more object regions may obscure other object regions from the line-of-sight view of an image acquisition camera's position. As a consequence, it may not be possible from a single camera position to simultaneously view related object features such as circumferential points of a complete cross-sectional object profile. In other words, unlike that of substantially two-dimensional objects or a two-dimensional object face, related features of a complicated and opaque three-dimensional object are not guaranteed to be together fully exposed for simultaneous image acquisition. Instead, only a portion of the object and a subset of related object features are likely to be fully exposed to a single image acquisition camera angle.
The complications of this scenario are compounded in many applications where the location of a single image acquisition camera is limited by the manufacturing environment; e.g., where an optimum camera location cannot be accommodated. For example, in a scenario where an orthogonal, top-down view of a three-dimensional object may be known to encompass a complete set of object features, such vertical location of the camera may not be practical. For a large class of manufacturing applications, accommodation can be made for only a single, oblique camera location that results in a acquisition of only a perspective view of an object; and for some camera angles this can result in a large fraction of related object features being obscured in the object image.
A further complication is added for machine vision applications in which an object to be viewed is moving, e.g., rotating, during the process being monitored. In this case, a different subset of related features, e.g., a different portion of an object's cross-sectional profile or shape, is in view of the camera's line-of-sight at any given time. Traditional vision system techniques, developed typically for orthogonal image acquisition and analysis of substantially two-dimensional object surfaces, are found to be ineffective at addressing these combinations of complicated object configurations, object movement, and manufacturing constraints using only a single image acquisition camera.